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InteractiveAI usecase Agent API

A minimal Flask service that each usecase can drop into their AI agent application. It receives a recommendation request from InteractiveAI and returns computed actions synchronously.

Features

  • Single endpoint: POST /v1/get_recommendations
  • Synchronous: compute and return actions in the same response
  • Strict inputs: use_case, context, event are required
  • Health check: GET /v1/healthz

Requirements

  • Python 3.10+
  • pip install flask

Run (development)

python app.py
# Service listens on http://0.0.0.0:8000

For production, use a proper WSGI server (e.g., gunicorn/uvicorn) and disable debug.

API

POST /v1/get_recommendations

Purpose: InteractiveAI sends a recommendation request. Your agent computes actions and returns them in the response.

Headers

  • Content-Type: application/json

Request Body (JSON)

  • Required
    • use_case (string) — e.g., Railway, Powergrid, ATM.
    • context (object) — Snapshot/state data from simulator.
    • event (object) — Trigger info (alarms, alerts, malfunctions, etc.).
  • Optional
    • agent_type (string, default: generic)
    • sim_api (object) — Hints/endpoints if your agent needs to fetch extra simulator data (left to usecase logic).

Response (200 OK, JSON)

  • title (string)
  • description (string)
  • use_case (string)
  • agent_type (string)
  • actions (array of objects) — computed by your agent logic

Error Responses

  • 400 Bad Request → {"error":"bad_request","message":"...","details":{"missing":[...]}}
  • 5xx → Unexpected server errors

GET /v1/healthz

  • Returns 200 OK with body ok.

Implementation Notes

  • Put your decision logic where the actions list is produced in get_recommendations.
  • If sim_api is present and your use case requires extra simulator data, fetch it before deciding actions (usecase-specific).
  • Security is intentionally omitted for this initial version; it will be added later.

File Overview

  • app.py
    • POST /v1/get_recommendations — main endpoint
    • GET /v1/healthz — readiness probe
    • Minimal 400 helper for validation errors

What Partners Must Implement

  • The decision logic producing the actions array.
  • (Optional) Simulator fetches based on sim_api.
  • (Later) Auth, retries, persistence, logging, and an async callback flow if needed.